GAN-based research is fertile and new architectures, loss functions, and tricks are being released on a daily basis. In this context of constant change, we enumerate a few questions that still need to be answered.
Unanswered questions in GANs
Are some losses better than others?
As we addressed earlier, in the paper, Are GANs Created Equal? A Large-Scale Study, the authors state that in their experiments, they did not find evidence that any of the tested algorithms consistently outperformed the non-saturating GAN. This leads us to wonder whether some losses are, in fact, better than others. We should bear this in mind when choosing a GAN framework.
